Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime

TL;DR

This study investigates the link between online conspiracy theory searches and offline hate crimes, using machine learning to predict violence based on online trends, revealing partial empirical connections for certain theories.

Contribution

Introduces a neural network approach to predict hate crimes from online conspiracy theory search data, highlighting specific theories with predictive power and advancing social science research.

Findings

Certain conspiracy theories predict hate crimes two to three weeks later

Most theories showed no clear connection to offline violence

Machine learning can identify online patterns linked to real-world harm

Abstract

Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. However, most theories showed no clear connection to offline hate crimes. Aligning with neutralization and differential association theories, our findings provide a partial empirical link between specific racially charged conspiracy theories and real-world violence. Just as well, this study underscores the potential for machine learning to be used in identifying harmful online patterns and advancing social science research.